Tools

by
Andrew Gelsey
- In Proceedings of the 11th International Joint Conference on Artificial Intelligence, 1989

"... The creation of abstract models of physical systems is an important AI research area. I describe a program which can automatically construct such models for machines like mechanical clocks or watches. The program finds an appropriate set of state variables and determines how they change as time pass ..."

The creation of abstract models of physical systems is an important AI research area. I describe a program which can automatically construct such models for machines like mechanical clocks or watches. The program finds an appropriate set of state variables and determines how they change as time passes. The abstract model of the mechanical device may be used to numerically simulate its behavior. My program uses short, controlled simulations to identify repet itive behavior patterns which can be used for long-term behavior prediction. 1

by
Richard J. Doyle, Suzanne M. Sellers, David J. Atkinson
- In Proceedings of the 11th International Conference on Artificial Intelligence, 1989

"... We address two issues which arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to utilize sensors selectively, so that nominal operation can be ve ..."

We address two issues which arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to utilize sensors selectively, so that nominal operation can be verified reliably without processing a prohibitive amount of sensor data. Our approach involves simulation of a causal model of the system, which provides information on expected sensor values, and on dependencies between predicted events, useful in assessing the relative importance of events so that sensor resources can be allocated effectively. 1. The Monitoring Problem Timely detection of anomalous behavior is essential for the continuous safe operation and longevity of aerospace systems. The pilot of a jet aircraft must be aware of any conditions which may affect thrust during the critical moments of takeoff. The thermal environment onboard Space Station Freedom must be carefully controlled to provide uninterrupted life support for the crew. The Mars Rover must react quickly to an unpredictable environment or the mission may come to an abrupt conclusion. Monitoring a physical system involves a number of problem-solving tasks. Dvorak, in his survey of work on expert systems for monitoring and control [Dvorak 87], lists among these tasks recognizing abnormal conditions, combining sensory information into a picture of the global state of a system, isolating faults, predicting both normal and faulted behavior, and maintaining safe operation in the presence of faults. In addition, decisions

"... Many large-scale industrial processes and services are centrally monitored and controlled under the supervision of trained operators. Common examples are electrical power plants, chemical refineries, air-traffic control, and telephone networks --- all impressively complex systems that are challengin ..."

Many large-scale industrial processes and services are centrally monitored and controlled under the supervision of trained operators. Common examples are electrical power plants, chemical refineries, air-traffic control, and telephone networks --- all impressively complex systems that are challenging to understand and operate correctly. The task of the operator is one of continuous, real-time monitoring and control, with feedback. The job can be difficult when the physical system is complex (tight coupling and complex interactions). Also, there may be faults not only in the system but also in its sensors and controls. Deciding the correct control action during a crisis can be difficult; a bad decision can be disastrous. This paper surveys existing work in the field of knowledge-based systems that assist plant/process operators in the task of monitoring and control. The goal here is to better define the information processing problems and identify key requirements for an automated opera...

...bove). When there are no primary events left to analyze, ESCORT continuously re-assesses its earlier diagnoses, checking to determine whether the problem symptoms still persist. 5.2.3 REACTOR REACTOR =-=[Nel82]-=- is an expert system developed to assist operators in the diagnosis and treatment of nuclear reactor accidents. The purpose of REACTOR is to monitor a nuclear reactor facility, detect deviations from ...

&apos;l&apos;lijs papcr dcscribcs a systcm called PDS, a forward chaining, rulc-bascd architccturc dcsigncd for die online, rcaltinic diagnosis of machine PTOCCS~CS. Two issues arisc in tlic application of cxpcrt sysrcins to the analysis of sensor-bascd data: spurious readings and sensor degradation. PDS implcmcnts techniqucs called rctrospcctive analysis and meta-diagnosis as solutions to these problems. lhese tcchniqucs and our expcrienccs in knowledge acquisition in a large organization, and the implementation of PDS as a portable

...ization, and the implementation of PDS as a portable diagnostic tool are described. 1 Introduction Research in the field of Al diagnosis systems has been evolving rapidly since the first event based (=-=Nelson, 1982-=-) or surface (Hart, 1982) reasoning systems (Shortliffe, 1976; Pople, 1977; Fox & Mostow, 1977; Duda et al., 1978), to systems that have functional or deep knowledge of their domain (Davis et al., 198...

"... Currently, the safety studies of the system (which are also collectively known as the safety case) cease or reduce in their utility after system certification, and with that, a vast amount of knowledge about the failure (or safe) behaviour of the system is usually rendered useless. In this thesis, w ..."

Currently, the safety studies of the system (which are also collectively known as the safety case) cease or reduce in their utility after system certification, and with that, a vast amount of knowledge about the failure (or safe) behaviour of the system is usually rendered useless. In this thesis, we argue that this knowledge could be usefully exploited in the context of an appropriate on-line safety monitoring scheme. As a practical application of our approach, we propose a safety monitor that operates on safety cases to support the on-line detection and control of hazardous failures in safety critical systems. Firstly,

"... The paper presents a novel expert system architecture which supports explicit representation and effective use of both declarative and procedural knowledge. These two types of expert knowledge are represented by means of production rules and event-graphs respectively, and they are processed by a uni ..."

The paper presents a novel expert system architecture which supports explicit representation and effective use of both declarative and procedural knowledge. These two types of expert knowledge are represented by means of production rules and event-graphs respectively, and they are processed by a unified inference engine. Communication between the rule level and the event-graph level is based on a full visibility of each level on the internal state of the other, and it is structured in such a way as to allow each level to exert control on the other. This structure offers several advantages over more traditional architectures. Knowledge representation is more natural and transparent; knowledge acquisition turns out to be easier as pieces of knowledge can be immediately represented without the need of complex transformation and restructuring; inference is more effective due to reduced non-determinism resulting from explicit representation of fragments of procedural knowledge in eventgraphs; finally, explanations are more natural and understandable. The proposed architecture has been adopted for the design of PROP, an expert system for on-line monitoring of the cycle water pollution in a thermal power plant. PROP is running on a SUN-2 workstation and has been tested on a sample of real cases.

...re introduced in the previous section. The usefulness of artificial intelligence techniques for assisting and advising the operator of a power plant in case of accidents has already been stressed in (=-=Nelson 1982-=-) and (Underwood 19B2). Our problem, even if it is not concerned with plant safety, lies in this application area. The aim of the system is to avoid damages to critical subsystems of the power plant a...

by
Michael J. Pazzani
- In Proceedings of the National Conference on Artificial Intelligence. American Association for Artificial Intelligence, 1986

"... This paper discusses an application of failure-driven learning to the construction of the knowledge base of a diagnostic expert system. Diagnosis heuristics (i.e., efficient rules which encode empirical associations between atypical device behavior and device failures) are learned from information i ..."

This paper discusses an application of failure-driven learning to the construction of the knowledge base of a diagnostic expert system. Diagnosis heuristics (i.e., efficient rules which encode empirical associations between atypical device behavior and device failures) are learned from information implicit in device models. This approach is desireable since less effort is required to obtain information about device functionality and connectivity to define device models than to encode and debug diagnosis heuristics from a domain expert, We give results of applying this technique in an expert system for the diagnosis of failures in the attitude control system of the DSCS-III satellite. The system is fully implemented in a combination of LISP and PROLOG on a Symbolics 3600. The results indicate that realistic applications can be built using this approach. The performance of the diagnostic expert system after learning is equivalent to and, in some cases, better than the performace of the expert system with rules supplied by a domain expert.

"... This r e s e a r c h concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers-F q have considerable experience in the application of advanced digita ..."

This r e s e a r c h concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers-F q have considerable experience in the application of advanced digital technologies and the protection of terrestrial power systems. This experience was used in the current contracts to develop new approaches for protecting the electrical distribution system in spaceborne applications. The project was divided into three distinct areas: 1. avestigate the applicability of fault detection algorithms developed for terrestrial power systems to the detection of faults in spaceborne systems; 2. 3. Investigate the digital hardware and architectures required to monitor and control spaceborne power systems with full capability to implement new detection and diagnostic algorithms,-d Develop a real-time expert operating system for implementing diagnostic and protection algorithms Significant progress has been made in each of the above areas. Several terrestrial fault detection algorithms were modified to better adapt to spaceborne power system environments. Several digital architectures were developed and evaluated in light of the fault detection algorithms. Also a parallelized rule-based system shell for monitoring and protection applications in real-time was developed. The system was based on CLIPS and has been designated PMCLIPS. ~ Le-.-

"... While one can characterize deep and shallow models at a high level of abstraction and contrast their relative merits in a general way, this provides little direction for knowledge engineering. In particular, the field lacks a clear definition of 'knowledge depth ' and lacks guidelines rega ..."

While one can characterize deep and shallow models at a high level of abstraction and contrast their relative merits in a general way, this provides little direction for knowledge engineering. In particular, the field lacks a clear definition of &apos;knowledge depth &apos; and lacks guidelines regarding the appropriate depth of models for a given application, in this paper we provide a very simple operational definition of knowledge depth &apos; and use it to examine the opportunities for varying depth in Intelligent safety systems. The paper illustrates a domain-independent mode of analysis for examining progressively deeper models of expertise, and sketches some domain-specific guidelines for constructing intelligent safety systems. We draw upon examples from the domains of nuclear reactor management, chemical plant control, and management of computer installation operations. 1.